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Article

Hybrid Sparsity Model for Fast Terahertz Imaging

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School of Artificial Intelligence and Big Data, Henan University of Technology, Zhengzhou 450001, China
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College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
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Key Laboratory of Grain Information Processing & Control, Ministry of Education, Henan University of Technology, Zhengzhou 450001, China
*
Author to whom correspondence should be addressed.
Academic Editor: Yi Yang
Micromachines 2021, 12(10), 1181; https://doi.org/10.3390/mi12101181
Received: 11 September 2021 / Revised: 28 September 2021 / Accepted: 28 September 2021 / Published: 29 September 2021
(This article belongs to the Section E:Engineering and Technology)
In order to shorten the long-term image acquisition time of the terahertz time domain spectroscopy imaging system while ensuring the imaging quality, a hybrid sparsity model (HSM) is proposed for fast terahertz imaging in this paper, which incorporates both intrinsic sparsity prior and nonlocal self-similarity constraints in a unified statistical model. In HSM, a weighted exponentiation shift-invariant wavelet transform is introduced to enhance the sparsity of the terahertz image. Simultaneously, the nonlocal self-similarity by means of the three-dimensional sparsity in the transform domain is exploited to ensure high-quality terahertz image reconstruction. Finally, a new split Bregman-based iteration algorithm is developed to solve the terahertz imaging model more efficiently. Experiments are presented to verify the effectiveness of the proposed approach. View Full-Text
Keywords: terahertz imaging; sparsity prior; nonlocal self-similarity; hybrid sparsity model; iteration algorithm terahertz imaging; sparsity prior; nonlocal self-similarity; hybrid sparsity model; iteration algorithm
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MDPI and ACS Style

Ren, X.; Bai, Y.; Jiang, Y. Hybrid Sparsity Model for Fast Terahertz Imaging. Micromachines 2021, 12, 1181. https://doi.org/10.3390/mi12101181

AMA Style

Ren X, Bai Y, Jiang Y. Hybrid Sparsity Model for Fast Terahertz Imaging. Micromachines. 2021; 12(10):1181. https://doi.org/10.3390/mi12101181

Chicago/Turabian Style

Ren, Xiaozhen, Yanwen Bai, and Yuying Jiang. 2021. "Hybrid Sparsity Model for Fast Terahertz Imaging" Micromachines 12, no. 10: 1181. https://doi.org/10.3390/mi12101181

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